java驗證碼識別--1 收藏
(本文僅用於學習研究影象匹配識別原理，不得用於其他用途。
最近看了看驗證碼的識別，先從最簡單的做起吧(固定大小，固定位置，固定字型)
驗證碼識別基本分四步，圖片預處理，分割，訓練，識別
看一個最簡單驗證碼
這是一個德克薩斯撲克的註冊頁面的驗證碼
1。影象的預處理
這種直接根據亮度設個閾值處理就可以了
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public static int isWhite(int colorInt) {
Color color = new Color(colorInt);
if (color.getRed() + color.getGreen() + color.getBlue() > 100) {
return 1;
}
return 0;
}
public static BufferedImage removeBackgroud(String picFile)
throws Exception {
BufferedImage img = ImageIO.read(new File(picFile));
int width = img.getWidth();
int height = img.getHeight();
for (int x = 0; x < width; ++x) {
for (int y = 0; y < height; ++y) {
if (isWhite(img.getRGB(x, y)) == 1) {
img.setRGB(x, y, Color.WHITE.getRGB());
} else {
img.setRGB(x, y, Color.BLACK.getRGB());
}
}
}
return img;
}
public static int isWhite(int colorInt) {
Color color = new Color(colorInt);
if (color.getRed() + color.getGreen() + color.getBlue() > 100) {
return 1;
}
return 0;
}
public static BufferedImage removeBackgroud(String picFile)
throws Exception {
BufferedImage img = ImageIO.read(new File(picFile));
int width = img.getWidth();
int height = img.getHeight();
for (int x = 0; x < width; ++x) {
for (int y = 0; y < height; ++y) {
if (isWhite(img.getRGB(x, y)) == 1) {
img.setRGB(x, y, Color.WHITE.getRGB());
} else {
img.setRGB(x, y, Color.BLACK.getRGB());
}
}
}
return img;
}
處理完圖片效果為
影象基本分得比較清楚，圖片分割也比較容易
2。分割
這個驗證碼居然是固定位置的，分割相當簡單，直接擷取相應位置就可以了
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public static List<BufferedImage> splitImage(BufferedImage img)
throws Exception {
List<BufferedImage> subImgs = new ArrayList<BufferedImage>();
subImgs.add(img.getSubimage(10, 6, 8, 10));
subImgs.add(img.getSubimage(19, 6, 8, 10));
subImgs.add(img.getSubimage(28, 6, 8, 10));
subImgs.add(img.getSubimage(37, 6, 8, 10));
return subImgs;
}
public static List<BufferedImage> splitImage(BufferedImage img)
throws Exception {
List<BufferedImage> subImgs = new ArrayList<BufferedImage>();
subImgs.add(img.getSubimage(10, 6, 8, 10));
subImgs.add(img.getSubimage(19, 6, 8, 10));
subImgs.add(img.getSubimage(28, 6, 8, 10));
subImgs.add(img.getSubimage(37, 6, 8, 10));
return subImgs;
}
3。訓練
直接拿幾張圖片，包含0-9，每個數字一個樣本就可以了，將檔名對應相應的數字
4。識別
因為是固定大小，固定位置，識別也很簡單。
直接拿分割的圖片跟這個十個圖片一個畫素一個畫素的比，相同的點最多的就是結果。比如如果跟5.jpg最相似，那麼識別的結果就是5。
下面是識別結果，很容易達到100%
完整程式碼(csdn連個附件都不支援)：
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import java.awt.Color;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.FileOutputStream;
import java.io.InputStream;
import java.io.OutputStream;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import javax.imageio.ImageIO;
import org.apache.commons.httpclient.HttpClient;
import org.apache.commons.httpclient.HttpStatus;
import org.apache.commons.httpclient.methods.GetMethod;
import org.apache.commons.io.IOUtils;
public class ImagePreProcess {
public static int isWhite(int colorInt) {
Color color = new Color(colorInt);
if (color.getRed() + color.getGreen() + color.getBlue() > 100) {
return 1;
}
return 0;
}
public static int isBlack(int colorInt) {
Color color = new Color(colorInt);
if (color.getRed() + color.getGreen() + color.getBlue() <= 100) {
return 1;
}
return 0;
}
public static BufferedImage removeBackgroud(String picFile)
throws Exception {
BufferedImage img = ImageIO.read(new File(picFile));
int width = img.getWidth();
int height = img.getHeight();
for (int x = 0; x < width; ++x) {
for (int y = 0; y < height; ++y) {
if (isWhite(img.getRGB(x, y)) == 1) {
img.setRGB(x, y, Color.WHITE.getRGB());
} else {
img.setRGB(x, y, Color.BLACK.getRGB());
}
}
}
return img;
}
public static List<BufferedImage> splitImage(BufferedImage img)
throws Exception {
List<BufferedImage> subImgs = new ArrayList<BufferedImage>();
subImgs.add(img.getSubimage(10, 6, 8, 10));
subImgs.add(img.getSubimage(19, 6, 8, 10));
subImgs.add(img.getSubimage(28, 6, 8, 10));
subImgs.add(img.getSubimage(37, 6, 8, 10));
return subImgs;
}
public static Map<BufferedImage, String> loadTrainData() throws Exception {
Map<BufferedImage, String> map = new HashMap<BufferedImage, String>();
File dir = new File("train");
File[] files = dir.listFiles();
for (File file : files) {
map.put(ImageIO.read(file), file.getName().charAt(0) + "");
}
return map;
}
public static String getSingleCharOcr(BufferedImage img,
Map<BufferedImage, String> map) {
String result = "";
int width = img.getWidth();
int height = img.getHeight();
int min = width * height;
for (BufferedImage bi : map.keySet()) {
int count = 0;
Label1: for (int x = 0; x < width; ++x) {
for (int y = 0; y < height; ++y) {
if (isWhite(img.getRGB(x, y)) != isWhite(bi.getRGB(x, y))) {
count++;
if (count >= min)
break Label1;
}
}
}
if (count < min) {
min = count;
result = map.get(bi);
}
}
return result;
}
public static String getAllOcr(String file) throws Exception {
BufferedImage img = removeBackgroud(file);
List<BufferedImage> listImg = splitImage(img);
Map<BufferedImage, String> map = loadTrainData();
String result = "";
for (BufferedImage bi : listImg) {
result += getSingleCharOcr(bi, map);
}
ImageIO.write(img, "JPG", new File("result//"+result+".jpg"));
return result;
}
public static void downloadImage() {
HttpClient httpClient = new HttpClient();
GetMethod getMethod = new GetMethod(
"http://www.puke888.com/authimg.php");
for (int i = 0; i < 30; i++) {
try {
// 執行getMethod
int statusCode = httpClient.executeMethod(getMethod);
if (statusCode != HttpStatus.SC_OK) {
System.err.println("Method failed: "
+ getMethod.getStatusLine());
}
// 讀取內容
String picName = "img//" + i + ".jpg";
InputStream inputStream = getMethod.getResponseBodyAsStream();
OutputStream outStream = new FileOutputStream(picName);
IOUtils.copy(inputStream, outStream);
outStream.close();
System.out.println("OK!");
} catch (Exception e) {
e.printStackTrace();
} finally {
// 釋放連線
getMethod.releaseConnection();
}
}
}
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
for (int i = 0; i < 30; ++i) {
String text = getAllOcr("img//" + i + ".jpg");
System.out.println(i + ".jpg = " + text);
}
}
}